{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.3 64-bit"
  },
  "interpreter": {
   "hash": "7d5c25a0dcd433e9174dbd40cc8162c4c651cca147fa234e40a811528338fdfd"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import os\n",
    "import time\n",
    "import copy\n",
    "import numpy as np\n",
    "#经典组合拳\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import torchvision\n",
    "from torchvision import datasets,transforms,models\n",
    "\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "#添加参数\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument(\"--batch_size\", type=int, default=4, help=\"size of the batches\")\n",
    "parser.add_argument(\"--img_size\", type=int, default=28, help=\"size of each image dimension\") #img_size搞大了它会自动插值\n",
    "opt = parser.parse_known_args()[0] # opt = parser.parse_args()\n",
    "\n",
    "#法一，使用torchvision.datasets中的数据集。\n",
    "dataset = {\n",
    "    'train':datasets.CIFAR10(\n",
    "        root=\"./cifar10/\",\n",
    "        train=True,\n",
    "        download=True,\n",
    "        transform=transforms.Compose([\n",
    "            transforms.Resize(opt.img_size), \n",
    "            transforms.ToTensor(), \n",
    "            transforms.Normalize([0.5], [0.5])\n",
    "        ]),\n",
    "    ),\n",
    "    'val':datasets.CIFAR10(\n",
    "        root=\"./cifar10/\",\n",
    "        train=False,\n",
    "        download=True,\n",
    "        transform=transforms.Compose([\n",
    "            transforms.Resize(opt.img_size), \n",
    "            transforms.ToTensor(), \n",
    "            transforms.Normalize([0.5], [0.5])\n",
    "        ]),\n",
    "    )\n",
    "}\n",
    "# b=dataset['train'].test_labels #注意这个是不区分test和train的。。\n",
    "# c=dataset['train'].train_labels #源码里，test_labels和train_labels指向同一处。\n",
    "dataloaders={\n",
    "    x:DataLoader(\n",
    "        dataset['train'],\n",
    "        batch_size=opt.batch_size,\n",
    "        shuffle=True,\n",
    "    ) \n",
    "    for x in ['train', 'val']\n",
    "}\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "170500096it [01:33, 1824790.10it/s] \n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sample_imgs,sample_labels=next(iter(dataloaders['train']))\n",
    "plot.title(sample_labels)\n",
    "plot.imshow(torchvision.utils.make_grid(sample_imgs).numpy().transpose(1,2,0))\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Object `DataLoader` not found.\n"
     ]
    }
   ],
   "source": [
    "#法二 使用别处的模型，\n",
    "#法三 如何读取torchvision的模型？\n",
    "#法四 自创建MyDataset\n",
    "#法五 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载预训练！进行fine tune！\n",
    "model_ft = models.resnet18(pretrained=True) #默认分521类\n",
    "model_ft.fc = nn.Linear(model_ft.fc.in_features, 10)\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(),lr=1e-3,betas=(0.9,0.999))\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train() \n",
    "            else:\n",
    "                model.eval()  \n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 0/24\n----------\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-91-cc88ea5f8bd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n\u001b[0m\u001b[0;32m      2\u001b[0m                        num_epochs=25)\n",
      "\u001b[1;32m<ipython-input-83-ac2005147ab0>\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[0;32m     33\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mphase\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'train'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m                         \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m                         \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m                 \u001b[0mrunning_loss\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Python\\Python38\\lib\\site-packages\\torch\\optim\\lr_scheduler.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m                 \u001b[0minstance\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_step_count\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m                 \u001b[0mwrapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[1;31m# Note that the returned function here is no longer a bound method,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Python\\Python38\\lib\\site-packages\\torch\\autograd\\grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Python\\Python38\\lib\\site-packages\\torch\\optim\\adam.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m             \u001b[0mbeta1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbeta2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'betas'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m             F.adam(params_with_grad,\n\u001b[0m\u001b[0;32m    109\u001b[0m                    \u001b[0mgrads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m                    \u001b[0mexp_avgs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Python\\Python38\\lib\\site-packages\\torch\\optim\\functional.py\u001b[0m in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad, beta1, beta2, lr, weight_decay, eps)\u001b[0m\n\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[1;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0mexp_avg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[0mexp_avg_sq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}